Artificial intelligence-based delay prediction models for signalized intersections in urban areas
Bibliographic record
Abstract
Intersections are significant road elements for traffic safety and road capacity. Furthermore, intersections have serious impacts on travel time. Time lost due to deceleration and stopping manoeuvres increases travel time and causes delays. Various factors affect the intersection delay. However, the effects of public transportation on delays also need to be investigated. This study focuses on these impacts on delays. The delays at four-legged-signalized-intersections were studied in the city-center-of Denizli, Türkiye. Intelligent Transportation System (ITS) was used to obtain information from both the traffic and public transportation systems, and a common database was built by cleaning and processing data. Multiple linear regression and artificial intelligence techniques were used to predict delays and then these methods were compared. The findings show that the k-nearest neighbor and artificial neural network give the best results with symmetric mean absolute percentage error values of 14.3% and 15.31%, respectively. In addition, the root mean square errors of these methods were found to be 10.47 and 10.42 s, respectively.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".